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README.md

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+---
+title: KisanAI
+emoji: 😻
+colorFrom: yellow
+colorTo: blue
+sdk: gradio
+sdk_version: 5.12.0
+app_file: app.py
+pinned: false
+license: mit
+short_description: ML based prediction system
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import pickle
+import numpy as np
+import os
+
+# Load the RandomForest model
+working_dir = os.path.dirname(os.path.abspath(__file__))
+model = pickle.load(open(f'{working_dir}/RF_Crop.sav', 'rb'))
+
+# Overview section content
+overview_text = """
+### Welcome to the Crop Recommendation App!
+
+This application assists farmers in selecting the optimal crop to cultivate, considering soil composition
+and environmental conditions. By providing information such as nitrogen, phosphorus, and
+potassium levels, as well as temperature, humidity, pH, and rainfall, users receive tailored
+recommendations for the most suitable crop out of a selection of 22 options.
+
+### How to Use the App
+1. Navigate to the "Crop Recommendation" section.
+2. Enter the values for the soil and environmental factors in the input fields.
+3. Click the "Predict" button to get the crop recommendation.
+
+### About the Model
+The recommendation is made using a Random Forest model trained on agricultural data.
+This model considers various factors to predict the best crop for your field.
+The model has been developed by analyzing many models like SVM, Random Forest,
+Decision Tree, Logistic Regression, Gaussian Naive Bayes. Random Forest has been selected based on
+the Cross Validation Accuracy & Test Accuracy.
+
+### Benefits of Using Crop Recommendation
+- **Increased Yield**: By planting the most suitable crop, you can maximize your harvest.
+- **Cost Efficiency**: Avoid wasting resources on crops that are not suited to your soil and climate.
+- **Sustainable Farming**: Promote better land use and reduce environmental impact.
+
+### Contact Us
+If you have any questions or feedback about the project, feel free to reach out:
+- **Email**: kanchanrai2307@gmail.com
+- **Github**: [kanchanrai7](https://github.com/kanchanrai7)
+"""
+
+# Define the prediction function
+def predict_crop(N, P, K, temperature, humidity, pH, rainfall):
+    user_input = np.array([[N, P, K, temperature, humidity, pH, rainfall]])
+    if np.all(user_input == 0):
+        return "Please enter valid values."
+    else:
+        prediction = model.predict(user_input)
+        crop = prediction[0]
+        return f"Hey, you should grow **{crop}** based on your soil and environmental factors."
+
+# Gradio UI components
+def main_interface():
+    with gr.Blocks() as demo:
+        with gr.Tab("Overview"):
+            gr.Markdown(overview_text)
+            gr.Image("Images/image1.jpg", label="Healthy Crops")
+            gr.Image("Images/mod_comparison.png", label="Model Comparison")
+        
+        with gr.Tab("Get Recommendation"):
+            gr.Markdown("Enter the details about your soil and environmental factors to get a crop recommendation.")
+            gr.Markdown("**Example Values:** [104, 18, 30, 23.6, 60.3, 6.7, 140.91] or [60, 18, 30, 23.6, 60.3, 8, 40.91]")
+
+            N = gr.Number(label="Nitrogen (N)", value=0, precision=0)
+            P = gr.Number(label="Phosphorus (P)", value=0, precision=0)
+            K = gr.Number(label="Potassium (K)", value=0, precision=0)
+            temperature = gr.Number(label="Temperature (°C)", value=0.0)
+            humidity = gr.Number(label="Humidity (%)", value=0.0)
+            pH = gr.Number(label="pH", value=0.0)
+            rainfall = gr.Number(label="Rainfall (mm)", value=0.0)
+
+            output = gr.Textbox(label="Recommendation", interactive=False)
+
+            gr.Button("Predict").click(
+                predict_crop, 
+                inputs=[N, P, K, temperature, humidity, pH, rainfall], 
+                outputs=output
+            )
+
+    return demo
+
+# Run the Gradio app
+if __name__ == "__main__":
+    app = main_interface()
+    app.launch()

requirements.txt

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+scikit-learn==1.2.2